Sunday 10 May 2009

Quickpost: Disinformational Tweets

This useless Python program is the result of some lazy Sunday coding. It will create random tweets based on a template file. You could use it to try to protect your privacy on Twitter by disinforming potential data miners.

Will I use it for my Twitter account? No, I don’t need a program to disinform 😉

Each time you run the program, it will post one random tweet. This tweet is generated from a templates file. Each line in the templates file is the template for a tweet. You can use variables (between curly braces, example: {location}) in the templates to increase the number of possible tweets. Variables and their values are also stored in the template file, after the template lines. Your template file must allow the program to generate at least 2 different tweets, because it generates a tweet different from the last tweet.

And you need to create a credentials file (disinformational-tweets.cred) with the Twitter credentials of the account for which the program has to generate random Tweets. The first line of the credentials file has to contain the username, the second line has to contain the password.

A Firefox plugin to generate these tweets would probably be more ‘useful’, but hey, it’s a lazy Sunday.

That’s funny. I was thinking of something very similar this morning. It was less focused on disinformational tweets themselves, and more focused on adding random people to your follow list to disrupt automatic social-network mapping attempts.

That’s true — depending on your personal guidelines for who you add or don’t add, that might or might not add much disinformation. Then again, if you have the timeline available for when someone added someone else, you might be able to more accurately map who was a “real” follow, versus who was just a reciprocating follow.

I’d be curious to see a study in the general case — what information is available, and to what degree it survives disinformation. I suspect that even with a fair amount of bogus data the good data can still be filtered through relatively effectively.